Data Engineer

Abbott Laboratories • Full-time • Remote (Abbott Park, Illinois, United States) • $60k - $120k / year • 23h ago

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 114,000 colleagues serve people in more than 160 countries.

We’re focused on helping people with diabetes manage their health with life-changing products that provide accurate data to drive better-informed decisions. We’re revolutionizing the way people monitor their glucose levels with our new sensing technology.

Working at Abbott

At Abbott, you can do work that matters, grow, and learn, care for yourself and family, be your true self and live a full life. You’ll also have access to:

Career development with an international company where you can grow the career you dream of.
Employees can qualify for free medical coverage in our Health Investment Plan (HIP) PPO medical plan in the next calendar year
An excellent retirement savings plan with high employer contribution
Tuition reimbursement, the Freedom 2 Save student debt program and FreeU education benefit - an affordable and convenient path to getting a bachelor’s degree.
A company recognized as a great place to work in dozens of countries around the world and named one of the most admired companies in the world by Fortune.
A company that is recognized as one of the best big companies to work for as well as a best place to work for diversity, working mothers, female executives, and scientists.

THE OPPORTUNITY

ThisData Engineer position can work out remotely within the U.S.

Are you passionate about using your technical expertise to make a real-world impact in healthcare? Join our team as a Data Engineer and help improve the lives of people with diabetes through data-driven innovation.

As a senior member of the Data Engineering & Analytics team, you’ll lead big data engineering and cloud-based analysis to support real-time decision-making, define scalable data strategies, and build secure, efficient pipelines. You’ll operationalize machine learning models, uncover insights across key domains, and collaborate with cross-functional teams to solve complex business challenges. The role involves working with a modern tech stack including Databricks, Redshift, S3, Lambda, DynamoDB, Spark, and Python.

We’re looking for someone versatile, passionate about software engineering, and eager to learn and empower others—helping shape data solutions that improve patient outcomes.

What You’ll Work On

Design and implement data pipelines to be processed and visualized across a variety of projects and initiatives
Develop and maintain optimal data pipeline architecture by designing and implementing data ingestion solutions on AWS using AWS native services.
Design and optimize data models on AWS Cloud using Databricks and AWS data stores such as Redshift, RDS, S3
Integrate and assemble large, complex data sets that meet a broad range of business requirements
Read, extract, transform, stage and load data to selected tools and frameworks as required and requested
Customizing and managing integration tools, databases, warehouses, and analytical systems
Process unstructured data into a form suitable for analysis and assist in analysis of the processed data
Work directly with the technology and engineering teams to integrate data processing and business objectives
Monitoring and optimizing data performance, uptime, and scale; Maintaining high standards of code quality and thoughtful design
Create software architecture and design documentation for the supported solutions and overall best practices and patterns
Support team with technical planning, design, and code reviews including peer code reviews
Provide Architecture and Technical Knowledge training and support for the solution groups
Develop good working relations with the other solution teams and groups, such as Engineering, Marketing, Product, Test, QA.
Stay current with emerging trends, making recommendations as needed to help the organization innovate

Qualifications

Bachelors Degree in Computer Science, Information Technology or other relevant field
At least 1-3 years of recent experience in Software Engineering, Data Engineering or Big Data
Ability to work effectively within a team in a fast-paced changing environment
Knowledge of or direct experience with Databricks and/or Spark.
Software development experience, ideally in Python, PySpark, Kafka or Go, and a willingness to learn new software development languages to meet goals and objectives.
Knowledge of strategies for processing large amounts of structured and unstructured data, including integrating data from multiple sources
Knowledge of data cleaning, wrangling, visualization and reporting
Ability to explore new alternatives or options to solve data mining issues, and utilize a combination of industry best practices, data innovations and experience
Familiarity of databases, BI applications, data quality and performance tuning
Excellent written, verbal and listening communication skills
Comfortable working asynchronously with a distributed team

Preferred

Knowledge of or direct experience with the following AWS Services desired S3, RDS, Redshift, DynamoDB, EMR, Glue, and Lambda.
Experience working in an agile environment
Practical Knowledge of Linux

#software

Apply Now

Learn more about our health and wellness benefits, which provide the security to help you and your family live full lives: www.abbottbenefits.com

Follow your career aspirations to Abbott for diverse opportunities with a company that can help you build your future and live your best life. Abbott is an Equal Opportunity Employer, committed to employee diversity.

Connect with us at www.abbott.com, on Facebook at www.facebook.com/Abbott and on Twitter @AbbottNews and @AbbottGlobal

The base pay for this position is $60,000.00 – $120,000.00. In specific locations, the pay range may vary from the range posted.